Method and system for a fast adaptation for image segmentation for autonomous edge vehicles

ABSTRACT

A method includes obtaining, by a local data system manager of a local data system of the local data systems, a portion of unlabeled data from a local data source, performing, using a domain classifier in the local data system manager, a domain classification analysis on the portion of the unlabeled data to identify a domain of the unlabeled data, making a first determination, based on the domain classification, that the domain classification has significantly varied from a previous domain, based on the first determination: performing an adaptive procedure on a local data system image segmentation model to obtain an adapted image segmentation model, and performing a domain reclassification on the domain classifier to obtain an updated domain classifier, and implementing the adapted image segmentation model on the local data system.

BACKGROUND

Systems may include multiple computing devices. Each computing devicemay include computing resources. The computing resources may be used toimplement generated models. The update of the generated models may incursome degree of latency and loss of accuracy.

SUMMARY

In general, in one aspect, the invention relates to a method formanaging local data systems. The method includes obtaining, by a localdata system manager of a local data system of the local data systems, aportion of unlabeled data from a local data source, performing, using adomain classifier in the local data system manager, a domainclassification analysis on the portion of the unlabeled data to identifya domain of the unlabeled data, making a first determination, based onthe domain classification, that the domain classification hassignificantly varied from a previous domain, based on the firstdetermination: performing an adaptive procedure on a local data systemimage segmentation model to obtain an adapted image segmentation model,and performing a domain reclassification on the domain classifier toobtain an updated domain classifier, and implementing the adapted imagesegmentation model on the local data system.

In general, in one aspect, the invention relates to a non-transitorycomputer readable medium comprising computer readable program code,which when executed by a computer processor enables the computerprocessor to perform a method for managing local data systems. Themethod includes obtaining, by a local data system manager of a localdata system of the local data systems, a portion of unlabeled data froma local data source, performing, using a domain classifier in the localdata system manager, a domain classification analysis on the portion ofthe unlabeled data to identify a domain of the unlabeled data, making afirst determination, based on the domain classification, that the domainclassification has significantly varied from a previous domain, based onthe first determination: performing an adaptive procedure on a localdata system image segmentation model to obtain an adapted imagesegmentation model, and performing a domain reclassification on thedomain classifier to obtain an updated domain classifier, andimplementing the adapted image segmentation model on the local datasystem.

In general, in one aspect, the invention relates to a system thatincludes an image segmentation manager, a local data system, and memorythat includes instructions, which when executed by the local datasystem, perform a method. The method includes obtaining, by a local datasystem manager of a local data system of the local data systems, aportion of unlabeled data from a local data source, performing, using adomain classifier in the local data system manager, a domainclassification analysis on the portion of the unlabeled data to identifya domain of the unlabeled data, making a first determination, based onthe domain classification, that the domain classification hassignificantly varied from a previous domain, based on the firstdetermination: performing an adaptive procedure on a local data systemimage segmentation model to obtain an adapted image segmentation model,and performing a domain reclassification on the domain classifier toobtain an updated domain classifier, and implementing the adapted imagesegmentation model on the local data system.

BRIEF DESCRIPTION OF DRAWINGS

Certain embodiments of the invention will be described with reference tothe accompanying drawings. However, the accompanying drawings illustrateonly certain aspects or implementations of the invention by way ofexample and are not meant to limit the scope of the claims.

FIG. 1A shows a diagram of a system in accordance with one or moreembodiments of the invention.

FIG. 1B shows a diagram of a local data system in accordance with one ormore embodiments of the invention.

FIG. 2A shows a flowchart for deploying an image segmentation base modelin accordance with one or more embodiments of the invention.

FIG. 2B shows a flowchart for performing an image segmentationadaptation in accordance with one or more embodiments of the invention.

FIGS. 3A-3C show examples in accordance with one or more embodiments ofthe invention.

FIG. 4 shows a diagram of a computing device in accordance with one ormore embodiments of the invention.

DETAILED DESCRIPTION

Specific embodiments will now be described with reference to theaccompanying figures. In the following description, numerous details areset forth as examples of the invention. It will be understood by thoseskilled in the art that one or more embodiments of the present inventionmay be practiced without these specific details and that numerousvariations or modifications may be possible without departing from thescope of the invention. Certain details known to those of ordinary skillin the art are omitted to avoid obscuring the description.

In the following description of the figures, any component describedwith regard to a figure, in various embodiments of the invention, may beequivalent to one or more like-named components described with regard toany other figure. For brevity, descriptions of these components will notbe repeated with regard to each figure. Thus, each and every embodimentof the components of each figure is incorporated by reference andassumed to be optionally present within every other figure having one ormore like-named components. Additionally, in accordance with variousembodiments of the invention, any description of the components of afigure is to be interpreted as an optional embodiment, which may beimplemented in addition to, in conjunction with, or in place of theembodiments described with regard to a corresponding like-namedcomponent in any other figure.

In general, embodiments of the invention relate to a method and systemfor managing local data systems. Specifically, embodiments of theinvention relate to a method of enabling a fast adaptation andimplementation of an image segmentation model executing on a local datasystem. The local data system (e.g., an autonomous vehicle edge device)may initially execute a base model obtained from an image segmentationmanager (e.g., implemented using a cloud service). The base model may begenerated using an initial set of labeled and unlabeled trainingdataset. The initial set of labeled and unlabeled training dataset maycorrespond to an initial domain.

In one or more embodiments, as a local data system continues operation,the domain of operation may change. As such, obtained images by thelocal data system may correspond to a varied domain. As a variancereaches a level of significance, the local data system monitoring thedomain of the obtained images may initiate an adaptation of the imagesegmentation model. The adaptation may include using a modified versionof domain adaptation neural networks (DANN).

FIG. 1A shows a diagram of a system in accordance with one or moreembodiments of the invention. The system includes an image segmentationmanager (120) and one or more local data systems (110). Each componentof the system may be operably connected via any combination of wiredand/or wireless connections. The system may include additional, fewer,and/or different components without departing from the invention. Eachcomponent of the system illustrated in FIG. 1A is discussed below.

In one or more embodiments of the invention, the image segmentationmanager (120) provides image segmentation services. The imagesegmentation services may include, for example, generating an imagesegmentation base model (124) and providing the image segmentation basemodel to a set of local data systems (112, 114). The image segmentationbase model (124) may be based on a neural network. In order to performthe aforementioned functionality, the image segmentation manager (120)includes an image segmentation engine (122) and storage (126). The imagesegmentation manager (120) may include additional, fewer, and/ordifferent components without departing from the invention. Eachcomponent of the image segmentation manager (120) illustrated in FIG. 1Ais discussed below.

In one or more embodiments of the invention, the image segmentationengine (122) generates the image segmentation base model (124). In oneor more embodiments of the invention, the image segmentation base model(124) is generated using the labeled training dataset (126A) and theunlabeled training dataset (126B). The image segmentation engine (122)may perform a neural network algorithm in accordance with FIG. 2A usingthe labeled training dataset (126A) and the unlabeled training dataset(126B).

In one or more embodiments of the invention, the storage (126) storesthe labeled training dataset (126A) and the unlabeled training dataset(126B). The labeled training dataset (126A) may include a set of dataitems used for training a machine learning algorithm The data items maybe, for example, images. The images may be labeled with individualobjects in the images. The individual objects may be used in conjunctionwith the unlabeled training data set (126B) for training the base model(124).

In one or more embodiments of the invention, the image segmentationmanager (120) is implemented as a computing device (see, e.g., FIG. 4).A computing device may be, for example, a mobile phone, tablet computer,laptop computer, desktop computer, server, or cloud resource. Thecomputing device may include one or more processors, memory (e.g.,random access memory), and persistent storage (e.g., disk drives, solidstate drives, etc.). The persistent storage may store computerinstructions, e.g., computer code, that when executed by theprocessor(s) of the computing device cause the computing device toperform the functions of the image segmentation manager (120) describedthroughout this application and/or all, or portion, of the methodillustrated in FIG. 2A.

In one or more embodiments of the invention, the image segmentationmanager (120) is implemented as a logical device. The logical device mayutilize computing resources of any number of physical computing devicesto provide the functionality of the image segmentation manager (120)described throughout this application.

In one or more embodiments of the invention, the local data systems(110) are devices accessible, at least in part, to the imagesegmentation manager (120). The local data systems (112, 114) may eachexecute an image segmentation model. The image segmentation model maybe, at least initially, the image segmentation base model (124)generated by the image segmentation engine (122) and distributed to thelocal data systems (110) in accordance with FIG. 2A.

In one or more embodiments of the invention, the local data systems(110) are implemented as vehicle edge devices. The vehicle edge devicesmay capture images during operation (e.g., while driving). Further, thevehicle edge devices may include functionality for operatingautonomously (e.g., self-driving). The vehicle edge devices may operateautonomously using the image segmentation model. For additional detailsregarding the local data systems (110), see, e.g., FIG. 1B.

The invention is not limited to the architecture shown in FIG. 1A.

FIG. 1B shows a diagram of a local data system (150) in accordance withone or more embodiments of the invention. The local data system (150)may be an embodiment of a local data system (112, 114, FIG. 1A)discussed above. As discussed above, the local data system (150)includes functionality for executing a local data system imagesegmentation model (166) during operation. Further, the local datasystem (150) includes functionality for monitoring obtained data todetect a change in domain (further discussed in FIG. 2B).

To perform the aforementioned functionality, the local data system (150)includes a set of local data sources (150A, 150B) and a local datasystem manager (160). The local data system (150) may includeadditional, fewer, and/or different components without departing fromthe invention. Each of the aforementioned components is discussed below.

In one or more embodiments of the invention, the local data sources(150A, 150B) are sources of data obtained from real-world processes. Alocal data source may be, for example, a camera. The data may be, forexample, images captured by the camera. The camera may be a hardwaredevice for capturing images during the operation of the local datasystem (150). Further, the local data sources (150A, 150B) may be, forexample, sensors. For example, the sensor may be a distance sensor. Thedistance sensor may detect the distance of objects relative to the localdata system (150) during operation.

In one or more embodiments of the invention, the local data systemmanager (160) includes functionality for monitoring data obtained fromthe local data sources (150A, 150B) and implementing a local data systemimage segmentation model (166). To perform the aforementionedfunctionality, the local data system manager (160) includes a datacollection manager (162), a domain classifier (164), the local datasystem image segmentation model (166), and storage (168). The local datasystem manager (160) may include additional, fewer, and/or differentcomponents without departing from the invention.

In one or more embodiments of the invention, the data collection manager(162) collects the data obtained from the local data sources (150A,150B) and provides it to the domain classifier (164) for additionalanalysis. Further, the data collection manager (162) may implement thelocal data system image segmentation model (166) using the obtaineddata. The implementation of the local data system image segmentationmodel (166) may include detecting (or otherwise discerning) objects inimages obtained from the local data sources (150A, 150B). The detectedobjects may be used for operation of the local data system.

For example, during operation of the local data system, the local datasystem may be driving along a road. The local data sources (150A, 150B)may capture images of its surroundings. The captured images may beprocessed via the local data system image segmentation model (166) todetect objects nearby (e.g., the road). The local data system (150),after detection of such images, may continue driving while stayingwithin the boundaries of the detected road and while avoiding collisionwith any additional detected objects (e.g., other cars).

While not illustrated in FIG. 1B, the local data system (150) mayinclude additional components to enable the operation of an autonomousvehicle. Examples of the additional components include, but are notlimited to: an engine, a transmission, axles, and wheels attached to theaxles.

In one or more embodiments of the invention, the data collection manager(162) is a hardware device including circuitry. The data collectionmanager (162) may be, for example, a digital signal processor, a fieldprogrammable gate array, or an application specific integrated circuit.The data collection manager (162) may be other types of hardware deviceswithout departing from the invention.

In one or more embodiments of the invention, the data collection manager(162) is implemented as computing code stored on a persistent storagethat when executed by a processor of the data collection manager (162)performs the functionality of the data collection manager (162). Theprocessor may be a hardware processor including circuitry such as, forexample, a central processing unit or a microcontroller. The processormay be other types of hardware devices for processing digitalinformation without departing from the invention.

In one or more embodiments of the invention, the domain classifier (164)is a component that monitors the data. The monitoring may includeperforming a domain classification analysis in accordance with FIG. 2A.

In one or more embodiments of the invention, the domain classifier (164)is a hardware device including circuitry. The domain classifier (164)may be, for example, a digital signal processor, a field programmablegate array, or an application specific integrated circuit. The domainclassifier (164) may be other types of hardware devices withoutdeparting from the invention.

In one or more embodiments of the invention, the domain classifier (164)is implemented as computing code stored on a persistent storage thatwhen executed by a processor of the domain classifier (164) performs thefunctionality of the domain classifier (164) discussed throughout theapplication. The processor may be a hardware processor includingcircuitry such as, for example, a central processing unit or amicrocontroller. The processor may be other types of hardware devicesfor processing digital information without departing from the invention.

In one or more embodiments of the invention, the local data system imagesegmentation model (166) is a model generated by implementing a machinelearning algorithm The model may obtain inputs (e.g., images) andprovide an output that includes labeled images that detect objectsand/or other characteristics in the obtained inputs.

In one or more embodiments of the invention, the local data system imagesegmentation model (166) is initially implemented as the base modelgenerated by an image segmentation manager (discussed above). Duringoperation of the local data system (150), the local data system imagesegmentation model (166) may be updated based on an adaptation of thedomain (discussed in FIG. 2B). The result may be an adapted local dataimage system image segmentation model.

In one or more embodiments of the invention, the storage (168) of thelocal data system (150) includes a subset of labeled training data(168A) and a subset of local data system unlabeled data (168P). Thelabeled training data subset (168A) may be a subset of the labeledtraining data set stored in the image segmentation manager. The localdata system unlabeled subset (168P) may be data obtained from the localdata source (150A, 150B). The storage (168) may include additional,fewer, and/or different data structures without departing from theinvention.

The invention may be implemented using other local data system resourceswithout departing from the invention.

FIGS. 2A-2B show flowcharts in accordance with one or more embodimentsof the invention. While the various steps in the flowcharts arepresented and described sequentially, one of ordinary skill in therelevant art will appreciate that some or all of the steps may beexecuted in different orders, may be combined or omitted, and some orall steps may be executed in parallel. In one embodiment of theinvention, the steps shown in FIGS. 2A-2B may be performed in parallelwith any other steps shown in FIGS. 2A-2B without departing from thescope of the invention.

FIG. 2A shows a flowchart for deploying an image segmentation base modelin accordance with one or more embodiments of the invention. The methodshown in FIG. 2A may be performed by, for example, an image segmentationmanager (e.g., 120, FIG. 1A). Other components of the system illustratedin FIG. 1A may perform the method of FIG. 2A without departing from theinvention.

Turning to FIG. 2A, in step 200, a labeled training dataset may beobtained by the image segmentation manager. In one or more embodimentsof the invention, the labeled training dataset may be obtained from anadministrator, or any other entity managing the generation of the imagesegmentation model. As discussed above, the labeled training data setmay include a number of data items (e.g., images) that are labeled withobjects in the images.

In step 202, an unlabeled training data set is obtained. Similar to thelabeled training dataset, the unlabeled training dataset may be obtainedfrom an administrator, or any other entity managing the generation ofthe image segmentation model. In contrast to the labeled trainingdataset, the data items of the unlabeled training dataset is not labeledwith objects.

In step 204, a neural network algorithm is performed on the labeledtraining dataset and the unlabeled training dataset to generate an imagesegmentation base model. In one or more embodiments of the invention,the neural network algorithm performed on the two datasets is a processof processing the data items in the two datasets using a networktopology configured by the administrator (or other entity).

In one or more embodiments of the invention, the neural networkalgorithm performed on the two datasets is a convolutional neuralnetwork (CNN). In one or more embodiments of the invention, a CNN is adeep neural network algorithm that includes extracting features from thetraining dataset (which may include the labeled and unlabeled trainingdatasets), filtering out, in one or more iterations, undesirable noisein a set of data items, and implementing a loss layer for determining adeviation between a predicted output and the true labels. As bothlabeled and unlabeled data items are used during one or more iterationsof the training, the CNN outputs an image segmentation base model thatvaries in accuracy and prediction time based on the number ofiterations. The image segmentation base model may be used to detectobjects in an unlabeled image input to the image segmentation basemodel.

In step 206, the image segmentation base model is deployed to a set ofone or more local data systems. In one or more embodiments of theinvention, the set of local data systems to which the image segmentationbase model is deployed is based on whether the local data systemrequests the image segmentation base model. Alternatively, the set oflocal data systems may be determined by the aforementioned administrator(or other entity). The set of local data systems to which the imagesegmentation base model may be determined by other mechanisms withoutdeparting from the invention.

FIG. 2B shows a flowchart for performing an image segmentationadaptation in accordance with one or more embodiments of the invention.The method shown in FIG. 2B may be performed by, for example, a localdata system (e.g., 150, FIG. 1B). Other components of the systemillustrated in FIG. 1A or FIG. 1B may perform the method of FIG. 2Bwithout departing from the invention.

In step 220, data corresponding to a local data system imagesegmentation model is obtained. In one or more embodiments of theinvention, the data is obtained from local data sources of the localdata system. For example, the data may be images provided from a videostream obtained from a video camera operating on the local data system.

In step 222, a domain classification analysis is performed to identify adomain on the data. In one or more embodiments of the invention, adomain is an overall classification of a type of environmentcorresponding to a data item (e.g., an image). The domain correspondingto an image may be impacted by a combination of factors such as, forexample: a road type in the image, weather conditions, number ofbuildings near the road, a location of the image, and size of thebuildings. Other factors may impact the domain of an image withoutdeparting from the invention. The image segmentation model implementedby the local data system may correspond to a first domain.

In one or more embodiments of the invention, the domain classificationanalysis may include comparing a domain of the obtained data classifiedusing the domain classification, and comparing the classified domain tothe first domain of the image segmentation model. Each domain maycorrespond to a set of numerical values. The numerical values may becompared to identify a variance of the two domains. A significantvariance may correspond to a variance that has reached and/or exceeded apredefined threshold.

In step 224, a determination is made about whether the domainclassification of the data indicates a significantly varied domain. Ifthe domain classification of the data indicates a significantly varieddomain, the method proceeds to step 226; otherwise, the method proceedsto wait for a predetermined amount of time prior to proceeding to step220.

In step 226, an adaptation procedure is performed on the local datasystem image segmentation model to obtain an adapted local data systemimage segmentation model. In one or more embodiments of the invention,the adaption procedure includes performing a modified version of theneural network algorithm performed in FIG. 2A.

In one or more embodiments of the invention, the modified version of theneural network is a domain adaptation neural network (DANN). The DANN isan algorithm performed that reduces the value of the labeled trainingdataset (due to the significant variation of the images in the labeledtraining dataset relative to the current domain of the new images). TheDANN utilizes a batch selection, modified feature extraction, a labelprediction mechanism, and the domain classifier.

In one or more embodiments of the invention, the batch selectionincludes obtaining a portion of the labeled training dataset from theimage segmentation manager and dividing such portion into groups oflabeled data. Further, the obtained data of step 220 is divided intogroups of unlabeled data. The groups of labeled data and the groups ofunlabeled data may be selected into batches. The batches may begenerated randomly. In other words the selection of each group of eitherlabeled or unlabeled data to a batch may be performed randomly.

Following the batch selection, a sampling ratio may be utilized todetermine an amount of data to be used for training. In one or moreembodiments of the invention, the sampling ratio is a ratio of unlabeleddata for each batch to be used for the training. A higher sampling ratioincreases the computation time during training, and thus increasing thetotal time performed during the adaptation procedure. Further, a highersampling ratio may increase an accuracy of the resulting adapted imagesegmentation model. An initial sampling ratio may be pre-determined bythe administrator.

Using the sampling ratio, a decay ratio is measured using a lossfunction applied to the data with the initial sampling ratio. In one ormore embodiments of the invention, a decay ratio is a rate of thesampling ratio over several iterations. The loss function may be ameasurement of an inaccuracy of the label prediction mechanism and thedomain classifier as a function of the sampled data and a set of machinelearning parameters. The decay ratio may be measured using a rate ofchange of loss over a number of iterations.

In one or more embodiments of the invention, the DANN further includesoptimizing a number of layers frozen during the neural network featureextraction phase of the training. In one or more embodiments of theinvention, the freezing of the number of layers includes reducing thenumber of layers to be used during the feature extraction. The accuracyand computation time may each be impacted based on the number of frozenlayers. For example, a trend of decreasing accuracy and decreasingcomputation time may occur as the number of frozen layers increases.Embodiments of the invention may include selecting an optimal number oflayers to freeze that reduces computation time while minimizing loss ofaccuracy.

In one or more embodiments of the invention, performing the trainingusing the modified feature extraction and based on a sampling ratioresults in an adapted local data system image segmentation model.

In step 228, the adapted local data system image segmentation model isimplemented. In one or more embodiments of the invention, the adaptedlocal data system image segmentation model replaces the previous imagesegmentation model.

In step 330, a domain reclassification is performed on the domainclassifier to update the domain classifier. In one or more embodimentsof the invention, the domain reclassification includes reclassifying theimages used to train the adapted local data system image segmentationmodel to generate a new domain to be used during the domainclassification analysis for new incoming data.

Example

The following section describes an example. The example, illustrated in

FIGS. 3A-3C, is not intended to limit the invention and is independentfrom any other examples discussed in this application. Turning to theexample, consider a scenario in which an autonomous vehicle edge deviceis implementing an image segmentation model while driving.

FIG. 3A shows an example system in accordance with one or moreembodiments of the invention. For the sake of brevity, not allcomponents of the example system may be illustrated. The example systemincludes a vehicle edge device (310) (also referred to as “vehicle”) andan image segmentation manager (300).

At a first point in time, an image segmentation engine (302) obtains thelabeled training dataset (326A) [1]. Further, the unlabeled trainingdataset (326B) is obtained [2]. A convolutional neural network (CNN) isapplied on the labeled training dataset (326A) and the unlabeledtraining dataset (326B) to obtain an image segmentation base model(304). The image segmentation manager (300) stores the base model (304)in the cloud service executing the image segmentation manager (300) [3].Further, the image segmentation manager (300) deploys the imagesegmentation base model to the vehicle edge device (310) [4]. Thevehicle edge device (310) stores the model as a local data system imagesegmentation model (312).

As shown in FIG. 3B, the vehicle edge device (310) includes a videocamera (310A) that captures images of the surroundings of the vehiclewhile driving. The images are to be processed using the imagesegmentation base model (312) deployed in FIG. 3A to be used to makedecisions while driving. As the vehicle edge device operates, thelocation in which the vehicle (310) drives outside of the city into arural area. The rural area may impact the domain associated with theimages obtained by the vehicle camera (310A).

During the operation, the video camera (310A) provides a set of imagesof the road in the rural area to the data collection manager (314) [5].The data collection manager (314) provides the images to the domainclassifier (316) [6]. The domain classifier (316) performs the method ofFIG. 2B to determine that the domain of the images have significantlyvaried from the domain of the labeled training data subset (168A)partially used to generate the image segmentation base model (312) [7].Based on this determination, the domain classifier (316) performs adomain adaptation in accordance with FIG. 2B. Specifically, the domainclassifier (316) obtains a second portion of labeled training dataset(306A) to be used for an adaption procedure performed on the imagesegmentation base model (312) [8]. Further, the domain classifier (316)obtains the recent set of images from the storage (318) in the vehicle(310) to be used during the adaptation procedure [9].

FIG. 3C shows the system at a later point in time. At this point intime, the vehicle edge device (310) generates the adapted imagesegmentation model (312) [10]. The vehicle edge device (310) nowoperates using the adapted image segmentation model (312) in the newrural environment.

End of Example

As discussed above, embodiments of the invention may be implementedusing computing devices. FIG. 4 shows a diagram of a computing device inaccordance with one or more embodiments of the invention. The computingdevice (400) may include one or more computer processors (402),non-persistent storage (404) (e.g., volatile memory, such as randomaccess memory (RAM), cache memory), persistent storage (406) (e.g., ahard disk, an optical drive such as a compact disk (CD) drive or digitalversatile disk (DVD) drive, a flash memory, etc.), a communicationinterface (412) (e.g., Bluetooth interface, infrared interface, networkinterface, optical interface, etc.), input devices (410), output devices(408), and numerous other elements (not shown) and functionalities. Eachof these components is described below.

In one embodiment of the invention, the computer processor(s) (402) maybe an integrated circuit for processing instructions. For example, thecomputer processor(s) may be one or more cores or micro-cores of aprocessor. The computing device (400) may also include one or more inputdevices (410), such as a touchscreen, keyboard, mouse, microphone,touchpad, electronic pen, or any other type of input device. Further,the communication interface (412) may include an integrated circuit forconnecting the computing device (400) to a network (not shown) (e.g., alocal area network (LAN), a wide area network (WAN) such as theInternet, mobile network, or any other type of network) and/or toanother device, such as another computing device.

In one embodiment of the invention, the computing device (400) mayinclude one or more output devices (408), such as a screen (e.g., aliquid crystal display (LCD), a plasma display, touchscreen, cathode raytube (CRT) monitor, projector, or other display device), a printer,external storage, or any other output device. One or more of the outputdevices may be the same or different from the input device(s). The inputand output device(s) may be locally or remotely connected to thecomputer processor(s) (402), non-persistent storage (404), andpersistent storage (406). Many different types of computing devicesexist, and the aforementioned input and output device(s) may take otherforms.

One or more embodiments of the invention may be implemented usinginstructions executed by one or more processors of the data managementdevice. Further, such instructions may correspond to computer readableinstructions that are stored on one or more non-transitory computerreadable mediums.

One or more embodiments of the invention may improve the operation ofone or more computing devices. More specifically, embodiments of theinvention distribute the load of adapting an image segmentation modelgenerated using a neural network machine learning algorithm to a deviceimplementing such image segmentation model. Specifically, embodiments ofthe invention enables a vehicle edge device, which may be operatingautonomously, to adapt to new environments by updating the model used todetect objects from images. By updating using a DANN machine learningalgorithm, less computation time is taken to perform the adaptation thana conventional neural network algorithm, thus reducing latency duringthe adaptation process.

While the invention has been described above with respect to a limitednumber of embodiments, those skilled in the art, having the benefit ofthis disclosure, will appreciate that other embodiments can be devisedwhich do not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims.

What is claimed is:
 1. A method for managing local data systems, themethod comprising: obtaining, by a local data system manager of a localdata system of the local data systems, a portion of unlabeled data froma local data source; performing, using a domain classifier in the localdata system manager, a domain classification analysis on the portion ofthe unlabeled data to identify a domain of the unlabeled data; making afirst determination, based on the domain classification, that the domainclassification has significantly varied from a previous domain; based onthe first determination: performing an adaptive procedure on a localdata system image segmentation model to obtain an adapted imagesegmentation model; and performing a domain reclassification on thedomain classifier to obtain an updated domain classifier; andimplementing the adapted image segmentation model on the local datasystem.
 2. The method of claim 1, wherein the local data system is anautonomous vehicle edge device.
 3. The method of claim 1, wherein theportion of the unlabeled data is a set of unlabeled images.
 4. Themethod of claim 1, wherein the local data system image segmentationmodel is obtained from an image segmentation manager.
 5. The method ofclaim 4, wherein the image segmentation manager is a cloud-basedservice.
 6. The method of claim 4, wherein performing the adaptiveprocedure comprises: obtaining a portion of a labeled training datasetfrom the image segmentation manager; dividing the portion of the labeledtraining dataset to obtain groups of labeled data; dividing the portionof unlabeled data to obtain groups of unlabeled data; implementing amachine learning algorithm on the groups of labeled data and the groupsof unlabeled data using the local data system image segmentation modelto obtain the adapted image segmentation model.
 7. The method of claim6, wherein the machine learning algorithm is a domain adaptation neuralnetwork (DANN), wherein the local data system image segmentation modelis generated using a convolutional neural network (CNN), and wherein theDANN comprises: determining a decay ratio; and optimizing a number oflayers in an extraction phase of a neural network using the decay ratio.8. A non-transitory computer readable medium comprising computerreadable program code, which when executed by a computer processorenables the computer processor to perform a method for managing localdata systems, the method comprising: obtaining, by a local data systemmanager of a local data system of the local data systems, a portion ofunlabeled data from a local data source; performing, using a domainclassifier in the local data system manager, a domain classificationanalysis on the portion of the unlabeled data to identify a domain ofthe unlabeled data; making a first determination, based on the domainclassification, that the domain classification has significantly variedfrom a previous domain; based on the first determination: performing anadaptive procedure on a local data system image segmentation model toobtain an adapted image segmentation model; and performing a domainreclassification on the domain classifier to obtain an updated domainclassifier; and implementing the adapted image segmentation model on thelocal data system.
 9. The non-transitory computer readable medium ofclaim 8, wherein the local data system is an autonomous vehicle edgedevice.
 10. The non-transitory computer readable medium of claim 8,wherein the portion of the unlabeled data is a set of unlabeled images.11. The non-transitory computer readable medium of claim 8, wherein thelocal data system image segmentation model is obtained from an imagesegmentation manager.
 12. The non-transitory computer readable medium ofclaim 11, wherein the image segmentation manager is a cloud-basedservice.
 13. The non-transitory computer readable medium of claim 11,wherein performing the adaptive procedure comprises: obtaining a portionof a labeled training dataset from the image segmentation manager;dividing the portion of the labeled training dataset to obtain groups oflabeled data; dividing the portion of unlabeled data to obtain groups ofunlabeled data; and implementing a machine learning algorithm on thegroups of labeled data and the groups of unlabeled data using the localdata system image segmentation model to obtain the adapted imagesegmentation model.
 14. The non-transitory computer readable medium ofclaim 13, wherein the machine learning algorithm is a domain adaptationneural network (DANN), wherein the local data system image segmentationmodel is generated using a convolutional neural network (CNN), andwherein the DANN comprises: determining a decay ratio; and optimizing anumber of layers in an extraction phase of a neural network using thedecay ratio.
 15. A system comprising: an image segmentation manager; alocal data system; and memory including instructions, which whenexecuted by the local data system, perform a method comprising:obtaining, by a local data system manager of the local data system, aportion of unlabeled data from a local data source; performing, using adomain classifier in the local data system manager, a domainclassification analysis on the portion of the unlabeled data to identifya domain of the unlabeled data; making a first determination, based onthe domain classification, that the domain classification hassignificantly varied from a previous domain; based on the firstdetermination: performing an adaptive procedure on a local data systemimage segmentation model to obtain an adapted image segmentation model;and performing a domain reclassification on the domain classifier toobtain an updated domain classifier; and implementing the adapted imagesegmentation model on the local data system.
 16. The system of claim 15,wherein the local data system is an autonomous vehicle edge device. 17.The system of claim 15, wherein the portion of the unlabeled data is aset of unlabeled images.
 18. The system of claim 15, wherein the localdata system image segmentation model is obtained from the imagesegmentation manager.
 19. The system of claim 18, wherein the imagesegmentation manager is a cloud-based service.
 20. The system of claim18, wherein performing the adaptive procedure comprises: obtaining aportion of a labeled training dataset from the image segmentationmanager; dividing the portion of the labeled training dataset to obtaingroups of labeled data; dividing the portion of unlabeled data to obtaingroups of unlabeled data; and implementing a machine learning algorithmon the groups of labeled data and the groups of unlabeled data using thelocal data system image segmentation model to obtain the adapted imagesegmentation model.